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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation for AI		- Data cleaning and transformation 1. Feature engineering and preparation 2. Data quality assurance
AI System Testing and Evaluation		- Model evaluation and monitoring 1. Explainability and reliability assessment 2. Performance evaluation and drift detection
AI Model Development and Iteration		- Model building and validation 1. Iterative delivery approach 2. Machine learning / generative AI model development
Data for AI		- Data identification and governance 1. Data compliance and ethics 2. Data sourcing and selection
Identify Business Needs and Solutions	26%	- Problem framing and business alignment 1. Feasibility and ROI analysis 2. Define AI business objectives
AI Operationalization and Governance		- Deployment and lifecycle management 1. Continuous improvement and monitoring 2. AI governance and responsible AI

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q79-Q84):

NEW QUESTION # 79
Your team is looking for a short term ROI project and decides that an AI enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition. What's the best course of action with how to proceed?

A. Do not move forward and cancel the project all together.
B. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go/No Go decision chart to start your project.
C. Move forward with the project as planned. The problem definition will become clear later on in the project.
D. Do not move forward until you can determine a clear problem definition.

Answer: D

Explanation:
A clear problem definition is essential before moving forward; without it, the project risks misalignment with business goals and wasted resources, so progress should be paused until clarity is achieved.

NEW QUESTION # 80
An AI project for a financial technology client is at risk due to potential inaccuracies in data aggregation. What is the first step the project manager should take to mitigate the risk?

A. Understand the data characteristics.
B. Delete the suspicious data manually.
C. Evaluate the data freshness and relevance.
D. Create a data visualization.

Answer: A

Explanation:
Understanding the data characteristics is the first step because the project manager must determine how the data is structured, sourced, aggregated, formatted, and related before identifying why inaccuracies may occur. This provides the foundation for evaluating freshness, relevance, cleansing needs, and aggregation risks.

NEW QUESTION # 81
During CPMAI Phase II it's important to not only understand the sources of your data, but also what data is required for training as well as identifying the features that are required. When looking to gather data, what approach is best when determining how much data you need?

A. The "more is better" approach
B. There is no correct approach
C. The "goldilocks" approach
D. The "less is better" approach

Answer: C

Explanation:
The "goldilocks" approach focuses on gathering an optimal amount of data - enough to train effective models without unnecessary excess - balancing quality and quantity for best results.

NEW QUESTION # 82
An IT services company is integrating an AI solution to automate its customer service functions.
The integration team is facing resistance from the customer's employees.
Which action should the project manager perform to manage this risk?

A. Offer the option to join another team
B. Implement a gradual phased rollout
C. Conduct all-hands meetings on the benefits
D. Mandate immediate transition from management

Answer: B

NEW QUESTION # 83
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs).
However, they are unsure how to effectively quantify these KPIs. Which two approaches should be used? (Choose two.)

A. Use random benchmarks without industry comparison.
B. Rely on only qualitative feedback from stakeholders.
C. Establish a baseline using historical data comparisons.
D. Implement a continuous performance monitoring system.
E. Set fixed performance targets based on theoretical models.

Answer: C,D

Explanation:
Establishing a baseline using historical data comparisons provides a measurable starting point for quantifying improvements in fraud detection accuracy, investigation time, and cost savings.
Continuous performance monitoring then tracks whether the AI system is meeting those KPI targets over time after implementation.

NEW QUESTION # 84
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